Top Machine Learning Engineer Jobs in San Francisco Bay Area, CA

Design, train, evaluate, and deploy machine learning and language models for real-time payments risk decisions. Own data pipelines, labeling, metrics, model evaluation, latency, and cost optimization. Convert user approvals into structured rules, detect fraud and prompt injection risks, assess identity and transaction signals, and make approve, confirm, or refuse decisions. The role is an individual contributor position with potential to build and manage a team.

Improve multimodal AI agents that analyze construction documents against regulatory requirements. Responsibilities include model post-training, experiment design, automated research, evaluation analysis, light data engineering, engineering tooling, web application improvements, and developing agents for new building types. The role also involves weekly user conversations and requires independent execution, rigorous evaluation standards, interest in housing supply and land-use reform, and willingness to work approximately 55 hours weekly.

Designs, scales, and leads machine learning systems that detect harmful content and bad actors, support content moderation, and promote platform integrity. Provides technical direction across the ML organization, partners with cross-functional leadership, improves ML infrastructure and operational excellence, and delivers performant, scalable models using modern deep learning techniques.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

Lead the technical direction and development of Uber’s real-time marketplace matching optimization systems. Build and productionize machine learning and decision systems using reinforcement learning, deep learning, causal inference, optimization, and control methods. Formulate ambiguous problems, develop behavioral models, create simulation and experimentation frameworks, and deploy scalable online inference systems. Collaborate cross-functionally, lead 0-to-1 initiatives, improve marketplace performance, and mentor engineers.

Build and operate production trust and safety systems that detect and disrupt platform abuse at internet scale. Responsibilities include developing ML infrastructure, training and feature pipelines, serving systems, evaluation frameworks, and LLM/classical ML detection solutions. Own systems from model integration through deployment, monitoring, and iteration while collaborating with security, product, and infrastructure teams.

Lead the design and operation of scalable machine learning infrastructure, including data generation, distributed training, production serving, evaluation consistency, accelerator optimization, reliability, and observability. Set technical direction, guide architecture, mentor engineers, and collaborate across modeling, evaluation, and platform teams. Build systems supporting autonomous driving models, simulation data, and large-scale production inference.

Develop and improve reliable LLM and deep learning capabilities, build scalable real-time machine learning models, advance Responsible AI practices, and define technical strategies for deep learning, RAG, and LLMs. Collaborate with AI scientists and engineers, conduct ML and reinforcement learning research, fine-tune models, and drive adoption of AI best practices across service teams.

Build and evaluate machine learning systems for an interactive consumer education product. Own AI features end to end, including goal setting, evaluation tooling, production deployment, orchestration, user modeling, personalization, feedback loops, experimentation, and quality, safety, latency, and engagement measurement. Partner with learning specialists and product engineers to deliver high-volume, low-latency language-model functionality.

Lead the architecture and technical strategy for high-fidelity machine learning perception models powering autonomous vehicle simulation ground truth. Build scalable foundation-model systems, optimize inference across petabyte-scale datasets, define quality and coverage roadmaps, align cross-functional stakeholders, and mentor perception engineers. The role requires deep expertise in computer vision, 3D perception, multimodal models, ML infrastructure, and production-scale inference.

Build and operate Philo’s ML and LLM infrastructure, including AWS and Kubernetes platforms, GPU scheduling, model serving, inference optimization, monitoring, evaluation, experimentation, and CI/CD. The role establishes infrastructure best practices, reliability, security, scalability, and Infrastructure as Code while supporting recommendation, video classification, and agent workloads. It also involves collaborating with engineers and vendors to deploy, instrument, and manage production systems.

Lead automated recommendation research and development through agentic AI, building LLM agents that generate hypotheses, implement changes, run experiments, and learn from results. Develop generative retrieval models using Semantic IDs, take research prototypes into production, improve ML infrastructure integrations, and evaluate system quality and impact. Provide hands-on technical leadership through architecture, implementation, reviews, documentation, mentorship, and cross-functional collaboration.

Build and lead production machine learning and evaluation systems for Atlassian’s Rovo AI agents. Responsibilities include developing datasets, simulations, automated judges, experimentation platforms, and quality signals; advancing reasoning, planning, retrieval, memory, and tool use; identifying and correcting systemic failures; and balancing quality, safety, latency, reliability, and cost. The role requires hands-on technical leadership, cross-functional influence, architectural decision-making, mentorship, and delivery of measurable customer outcomes at scale.

Lead the design and development of production-grade machine learning systems, including large-scale document pipelines and agentic workflows for healthcare claims and payment integrity. Drive LLM prompt engineering, multimodal AI, RAG, orchestration, observability, reliability, and explainability. Collaborate with data science, data engineering, and product teams, reduce technical debt, improve scalability, and mentor engineers on enterprise AI system design and development.

Design and scale machine learning infrastructure for personalization, search, ranking, advertising technology, and real-time learning at consumer scale. Build distributed systems, data pipelines, feature infrastructure, model-serving systems, and low-latency decision systems. Lead architecture and zero-to-one initiatives, partner across product and engineering teams, mentor technical leaders, improve experimentation velocity, and explore LLM applications for personalization and conversational search.

Own machine learning for physical robots, including data pipeline development, dataset validation, model fine-tuning, training workflows, hardware calibration, and deployment of learned control policies. Improve data and training standards, ensure reliable performance on real robotic systems, and evaluate emerging robot-learning research. The role requires hands-on robotics and applied machine learning engineering experience, with opportunities to support government-related projects.

AI/ML engineering intern will help design, build, evaluate, and deploy LLM-powered applications and machine learning systems. Responsibilities include developing data pipelines, working with structured and unstructured data, supporting model training and optimization, preparing systems for production monitoring, and collaborating with engineers, researchers, and product teams. The role offers mentorship and hands-on experience applying AI to healthcare, government, and energy workflows.

Build, deploy, and maintain machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Use AI tools to develop scalable services while ensuring code correctness, security, performance, and architectural quality. Mentor collaborators and solve ambiguous, large-scale problems.

Build and deploy machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Develop scalable, secure, production-ready services using AI-assisted engineering workflows, while contributing to ranking, recommendations, search, content understanding, or image-generation applications.

Design and build realistic simulated workplace environments, evaluation tasks, benchmarks, graders, and scoring systems for training and assessing AI models. Generate synthetic records, files, and communications; prevent models from exploiting evaluation systems; align benchmarks with real-world performance; and convert user feedback into new test cases. The role is early-career and emphasizes reinforcement learning or model post-training experience.

Develop core machine learning algorithms for unstructured data, including representation learning, clustering, outlier detection, active learning, and label-quality improvement. Apply deep learning research to customer problems, collaborate on product scalability, evolve the engineering stack, and mentor team members. The role uses Python and modern ML infrastructure across AWS, Kubernetes, FastAPI, and related technologies.

Own the ML platform layer connecting data science and infrastructure. Responsibilities include managing the feature store, building self-service training and deployment platforms, standardizing retraining, scaling batch and online inference, implementing monitoring, and establishing standards for model and feature versioning, lineage, testing, and documentation. The role partners closely with data scientists and backend engineers to improve model delivery speed, reliability, and independence across multiple teams.

Lead the design, development, deployment, and operation of machine learning models and platforms at scale. Build distributed ML infrastructure, data pipelines, cloud-based architectures, and production services. Develop and test application code, automate deployment, monitor and retrain models, and apply CI/CD, governance, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Build and evolve machine learning tooling and infrastructure for AI-powered clinical solutions, including automated prompt tuning, feedback loops, model benchmarking, experimentation, and evaluation. Partner with data science, clinical, software engineering, and product teams to develop scalable solutions, solve ambiguous technical problems, establish engineering standards, and provide technical mentorship without direct management responsibility.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.